Overview

Dataset statistics

Number of variables17
Number of observations8000
Missing cells65775
Missing cells (%)48.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory136.0 B

Variable types

NUM8
CAT8
BOOL1

Warnings

Fur Color has a high cardinality: 344 distinct values High cardinality
Ranking is highly correlated with Rarity ScoreHigh correlation
Rarity Score is highly correlated with RankingHigh correlation
Rarity has 4385 (54.8%) missing values Missing
Rarity Score has 4385 (54.8%) missing values Missing
Ranking has 4385 (54.8%) missing values Missing
Gender has 4385 (54.8%) missing values Missing
Breed has 4385 (54.8%) missing values Missing
Fur Color has 4385 (54.8%) missing values Missing
Eyes Color has 4385 (54.8%) missing values Missing
Dominant Personality has 4385 (54.8%) missing values Missing
Recessive Personality has 4385 (54.8%) missing values Missing
Vitality has 4385 (54.8%) missing values Missing
Intelligence has 4385 (54.8%) missing values Missing
Robustness has 4385 (54.8%) missing values Missing
Obedience has 4385 (54.8%) missing values Missing
Friendliness has 4385 (54.8%) missing values Missing
Bonding has 4385 (54.8%) missing values Missing
Id has unique values Unique

Reproduction

Analysis started2022-03-06 02:26:58.742086
Analysis finished2022-03-06 02:27:14.713347
Duration15.97 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Id
Real number (ℝ≥0)

UNIQUE

Distinct8000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4000.5
Minimum1
Maximum8000
Zeros0
Zeros (%)0.0%
Memory size62.5 KiB
2022-03-05T21:27:14.786153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile400.95
Q12000.75
median4000.5
Q36000.25
95-th percentile7600.05
Maximum8000
Range7999
Interquartile range (IQR)3999.5

Descriptive statistics

Standard deviation2309.54541
Coefficient of variation (CV)0.5773141882
Kurtosis-1.2
Mean4000.5
Median Absolute Deviation (MAD)2000
Skewness0
Sum32004000
Variance5334000
MonotocityStrictly increasing
2022-03-05T21:27:14.932788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20491< 0.1%
 
6611< 0.1%
 
68301< 0.1%
 
6851< 0.1%
 
27321< 0.1%
 
47751< 0.1%
 
68221< 0.1%
 
6771< 0.1%
 
27241< 0.1%
 
47671< 0.1%
 
Other values (7990)799099.9%
 
ValueCountFrequency (%) 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
51< 0.1%
 
ValueCountFrequency (%) 
80001< 0.1%
 
79991< 0.1%
 
79981< 0.1%
 
79971< 0.1%
 
79961< 0.1%
 

Reveal Status
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.5 KiB
2
3615 
0
3511 
1
874 
ValueCountFrequency (%) 
2361545.2%
 
0351143.9%
 
187410.9%
 
2022-03-05T21:27:15.077374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-03-05T21:27:15.149182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:15.224979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Rarity
Categorical

MISSING

Distinct4
Distinct (%)0.1%
Missing4385
Missing (%)54.8%
Memory size62.5 KiB
Bronze
2129 
Silver
1109 
Gold
294 
Diamond
 
83
ValueCountFrequency (%) 
Bronze212926.6%
 
Silver110913.9%
 
Gold2943.7%
 
Diamond831.0%
 
(Missing)438554.8%
 
2022-03-05T21:27:15.334686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-03-05T21:27:15.411514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:15.497284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length3
Mean length4.2925
Min length3

Rarity Score
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct79
Distinct (%)2.2%
Missing4385
Missing (%)54.8%
Infinite0
Infinite (%)0.0%
Mean51.22655602
Minimum20
Maximum98
Zeros0
Zeros (%)0.0%
Memory size62.5 KiB
2022-03-05T21:27:15.624911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile31
Q140
median48
Q361
95-th percentile81
Maximum98
Range78
Interquartile range (IQR)21

Descriptive statistics

Standard deviation15.2515837
Coefficient of variation (CV)0.2977280709
Kurtosis0.01898083944
Mean51.22655602
Median Absolute Deviation (MAD)9
Skewness0.6927943265
Sum185184
Variance232.6108054
MonotocityNot monotonic
2022-03-05T21:27:15.763573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
471261.6%
 
441231.5%
 
461171.5%
 
381171.5%
 
431161.5%
 
451131.4%
 
411121.4%
 
391071.3%
 
481041.3%
 
421011.3%
 
Other values (69)247931.0%
 
(Missing)438554.8%
 
ValueCountFrequency (%) 
2070.1%
 
2150.1%
 
2280.1%
 
2380.1%
 
2490.1%
 
ValueCountFrequency (%) 
981< 0.1%
 
9740.1%
 
962< 0.1%
 
9570.1%
 
94120.1%
 

Ranking
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct79
Distinct (%)2.2%
Missing4385
Missing (%)54.8%
Infinite0
Infinite (%)0.0%
Mean3868.074412
Minimum1
Maximum7983
Zeros0
Zeros (%)0.0%
Memory size62.5 KiB
2022-03-05T21:27:16.044787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile353
Q11834
median3840
Q35887
95-th percentile7478
Maximum7983
Range7982
Interquartile range (IQR)4053

Descriptive statistics

Standard deviation2292.619734
Coefficient of variation (CV)0.5927031102
Kurtosis-1.183530411
Mean3868.074412
Median Absolute Deviation (MAD)2006
Skewness0.05720686741
Sum13983089
Variance5256105.247
MonotocityNot monotonic
2022-03-05T21:27:16.192393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
40721261.6%
 
48691231.5%
 
43361171.5%
 
63531171.5%
 
51111161.5%
 
45991131.4%
 
56281121.4%
 
61221071.3%
 
38401041.3%
 
53661011.3%
 
Other values (69)247931.0%
 
(Missing)438554.8%
 
ValueCountFrequency (%) 
11< 0.1%
 
340.1%
 
72< 0.1%
 
1670.1%
 
35120.1%
 
ValueCountFrequency (%) 
798370.1%
 
796750.1%
 
795080.1%
 
792680.1%
 
790090.1%
 

Gender
Categorical

MISSING

Distinct2
Distinct (%)0.1%
Missing4385
Missing (%)54.8%
Memory size62.5 KiB
Male
1837 
Female
1778 
ValueCountFrequency (%) 
Male183723.0%
 
Female177822.2%
 
(Missing)438554.8%
 
2022-03-05T21:27:16.325038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-03-05T21:27:16.393855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:16.468656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length3
Mean length3.896375
Min length3

Breed
Categorical

MISSING

Distinct10
Distinct (%)0.3%
Missing4385
Missing (%)54.8%
Memory size62.5 KiB
German Shepherd
383 
Border Collie
383 
Labrador
376 
Toy Poodle
366 
Pomeranian Spitz
363 
Other values (5)
1744 
ValueCountFrequency (%) 
German Shepherd3834.8%
 
Border Collie3834.8%
 
Labrador3764.7%
 
Toy Poodle3664.6%
 
Pomeranian Spitz3634.5%
 
French Bulldog3584.5%
 
Shiba Inu3584.5%
 
Golden Retriever3494.4%
 
Rottweiler3414.3%
 
Husky3384.2%
 
(Missing)438554.8%
 
2022-03-05T21:27:16.587336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-03-05T21:27:16.679092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:16.829721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length16
Median length3
Mean length6.909125
Min length3

Fur Color
Categorical

HIGH CARDINALITY
MISSING

Distinct344
Distinct (%)9.5%
Missing4385
Missing (%)54.8%
Memory size62.5 KiB
Black & white #2
 
47
Black & white #3
 
42
Black & white #11
 
39
Black & white #1
 
39
Black & white #7
 
39
Other values (339)
3409 
ValueCountFrequency (%) 
Black & white #2470.6%
 
Black & white #3420.5%
 
Black & white #11390.5%
 
Black & white #1390.5%
 
Black & white #7390.5%
 
Black & white #8370.5%
 
Black & white #4370.5%
 
Black #8360.4%
 
Black & white #12350.4%
 
Black #7340.4%
 
Other values (334)323040.4%
 
(Missing)438554.8%
 
2022-03-05T21:27:16.963331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique36 ?
Unique (%)1.0%
2022-03-05T21:27:17.091989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length27
Median length3
Mean length7.099625
Min length3

Eyes Color
Categorical

MISSING

Distinct45
Distinct (%)1.2%
Missing4385
Missing (%)54.8%
Memory size62.5 KiB
Brown #3
322 
Brown #4
295 
Brown #5
290 
Brown #1
286 
Brown #2
267 
Other values (40)
2155 
ValueCountFrequency (%) 
Brown #33224.0%
 
Brown #42953.7%
 
Brown #52903.6%
 
Brown #12863.6%
 
Brown #22673.3%
 
Black #31882.4%
 
Black #41832.3%
 
Black #11752.2%
 
Black #51732.2%
 
Black #21622.0%
 
Other values (35)127415.9%
 
(Missing)438554.8%
 
2022-03-05T21:27:17.224662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)0.1%
2022-03-05T21:27:17.353288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length16
Median length3
Mean length5.496625
Min length3

Dominant Personality
Categorical

MISSING

Distinct30
Distinct (%)0.8%
Missing4385
Missing (%)54.8%
Memory size62.5 KiB
Playful
 
210
Funny
 
193
Talkative
 
193
Naive
 
193
Shy
 
191
Other values (25)
2635 
ValueCountFrequency (%) 
Playful2102.6%
 
Funny1932.4%
 
Talkative1932.4%
 
Naive1932.4%
 
Shy1912.4%
 
Charming1902.4%
 
Stubborn 1852.3%
 
Lazy1782.2%
 
Needy1762.2%
 
Foodie1752.2%
 
Other values (20)173121.6%
 
(Missing)438554.8%
 
2022-03-05T21:27:17.492950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-03-05T21:27:17.618581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length12
Median length3
Mean length4.6105
Min length3

Recessive Personality
Categorical

MISSING

Distinct30
Distinct (%)0.8%
Missing4385
Missing (%)54.8%
Memory size62.5 KiB
Needy
 
202
Gentle
 
199
Devoted
 
195
Foodie
 
189
Lazy
 
189
Other values (25)
2641 
ValueCountFrequency (%) 
Needy2022.5%
 
Gentle1992.5%
 
Devoted1952.4%
 
Foodie1892.4%
 
Lazy1892.4%
 
Talkative1892.4%
 
Funny1882.4%
 
Shy1872.3%
 
Naive1802.2%
 
Jolly1802.2%
 
Other values (20)171721.5%
 
(Missing)438554.8%
 
2022-03-05T21:27:17.744245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-03-05T21:27:17.865950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length12
Median length3
Mean length4.566875
Min length3

Vitality
Real number (ℝ≥0)

MISSING

Distinct12
Distinct (%)0.3%
Missing4385
Missing (%)54.8%
Infinite0
Infinite (%)0.0%
Mean6.169571231
Minimum3
Maximum14
Zeros0
Zeros (%)0.0%
Memory size62.5 KiB
2022-03-05T21:27:17.956675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q14
median6
Q38
95-th percentile11
Maximum14
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.347215899
Coefficient of variation (CV)0.3804504092
Kurtosis0.01111690627
Mean6.169571231
Median Absolute Deviation (MAD)2
Skewness0.7166468694
Sum22303
Variance5.509422478
MonotocityNot monotonic
2022-03-05T21:27:18.054413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
56508.1%
 
65897.4%
 
45617.0%
 
74405.5%
 
34245.3%
 
83254.1%
 
92943.7%
 
101261.6%
 
11981.2%
 
12680.9%
 
Other values (2)400.5%
 
(Missing)438554.8%
 
ValueCountFrequency (%) 
34245.3%
 
45617.0%
 
56508.1%
 
65897.4%
 
74405.5%
 
ValueCountFrequency (%) 
143< 0.1%
 
13370.5%
 
12680.9%
 
11981.2%
 
101261.6%
 

Intelligence
Real number (ℝ≥0)

MISSING

Distinct13
Distinct (%)0.4%
Missing4385
Missing (%)54.8%
Infinite0
Infinite (%)0.0%
Mean5.925587828
Minimum3
Maximum15
Zeros0
Zeros (%)0.0%
Memory size62.5 KiB
2022-03-05T21:27:18.146202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q14
median5
Q38
95-th percentile12
Maximum15
Range12
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.702300928
Coefficient of variation (CV)0.4560393005
Kurtosis0.08110590641
Mean5.925587828
Median Absolute Deviation (MAD)2
Skewness0.894341653
Sum21421
Variance7.302430306
MonotocityNot monotonic
2022-03-05T21:27:18.242912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
382610.3%
 
45426.8%
 
55266.6%
 
64685.9%
 
73334.2%
 
82783.5%
 
92232.8%
 
101281.6%
 
121091.4%
 
111041.3%
 
Other values (3)781.0%
 
(Missing)438554.8%
 
ValueCountFrequency (%) 
382610.3%
 
45426.8%
 
55266.6%
 
64685.9%
 
73334.2%
 
ValueCountFrequency (%) 
151< 0.1%
 
14270.3%
 
13500.6%
 
121091.4%
 
111041.3%
 

Robustness
Real number (ℝ≥0)

MISSING

Distinct11
Distinct (%)0.3%
Missing4385
Missing (%)54.8%
Infinite0
Infinite (%)0.0%
Mean5.25670816
Minimum3
Maximum13
Zeros0
Zeros (%)0.0%
Memory size62.5 KiB
2022-03-05T21:27:18.471301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q13
median4
Q37
95-th percentile11
Maximum13
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.487699392
Coefficient of variation (CV)0.4732428196
Kurtosis0.03190614034
Mean5.25670816
Median Absolute Deviation (MAD)1
Skewness1.060492019
Sum19003
Variance6.188648264
MonotocityNot monotonic
2022-03-05T21:27:18.570036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
3117114.6%
 
47038.8%
 
55296.6%
 
62993.7%
 
81922.4%
 
71912.4%
 
91742.2%
 
101662.1%
 
111411.8%
 
12480.6%
 
(Missing)438554.8%
 
ValueCountFrequency (%) 
3117114.6%
 
47038.8%
 
55296.6%
 
62993.7%
 
71912.4%
 
ValueCountFrequency (%) 
131< 0.1%
 
12480.6%
 
111411.8%
 
101662.1%
 
91742.2%
 

Obedience
Real number (ℝ≥0)

MISSING

Distinct12
Distinct (%)0.3%
Missing4385
Missing (%)54.8%
Infinite0
Infinite (%)0.0%
Mean6.316182573
Minimum3
Maximum14
Zeros0
Zeros (%)0.0%
Memory size62.5 KiB
2022-03-05T21:27:18.672760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q14
median6
Q38
95-th percentile11
Maximum14
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.569972799
Coefficient of variation (CV)0.4068870349
Kurtosis-0.8679466779
Mean6.316182573
Median Absolute Deviation (MAD)2
Skewness0.3464226647
Sum22833
Variance6.604760188
MonotocityNot monotonic
2022-03-05T21:27:18.777480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
36888.6%
 
44375.5%
 
84285.3%
 
54255.3%
 
64105.1%
 
74085.1%
 
93464.3%
 
102623.3%
 
111351.7%
 
12530.7%
 
Other values (2)230.3%
 
(Missing)438554.8%
 
ValueCountFrequency (%) 
36888.6%
 
44375.5%
 
54255.3%
 
64105.1%
 
74085.1%
 
ValueCountFrequency (%) 
142< 0.1%
 
13210.3%
 
12530.7%
 
111351.7%
 
102623.3%
 

Friendliness
Real number (ℝ≥0)

MISSING

Distinct13
Distinct (%)0.4%
Missing4385
Missing (%)54.8%
Infinite0
Infinite (%)0.0%
Mean5.146334716
Minimum3
Maximum15
Zeros0
Zeros (%)0.0%
Memory size62.5 KiB
2022-03-05T21:27:18.880208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q13
median4
Q37
95-th percentile11
Maximum15
Range12
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.863409068
Coefficient of variation (CV)0.5563977522
Kurtosis0.4456027655
Mean5.146334716
Median Absolute Deviation (MAD)1
Skewness1.241612757
Sum18604
Variance8.199111493
MonotocityNot monotonic
2022-03-05T21:27:18.984961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
3175922.0%
 
43704.6%
 
53113.9%
 
62252.8%
 
71962.5%
 
81772.2%
 
101622.0%
 
91421.8%
 
111151.4%
 
12951.2%
 
Other values (3)630.8%
 
(Missing)438554.8%
 
ValueCountFrequency (%) 
3175922.0%
 
43704.6%
 
53113.9%
 
62252.8%
 
71962.5%
 
ValueCountFrequency (%) 
1540.1%
 
14240.3%
 
13350.4%
 
12951.2%
 
111151.4%
 

Bonding
Boolean

MISSING

Distinct1
Distinct (%)< 0.1%
Missing4385
Missing (%)54.8%
Memory size62.5 KiB
1
3615 
(Missing)
4385 
ValueCountFrequency (%) 
1361545.2%
 
(Missing)438554.8%
 
2022-03-05T21:27:19.054773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Interactions

2022-03-05T21:27:05.446128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:05.666541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:05.831129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:05.997653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:06.157226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:06.313837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:06.432492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:06.558155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:06.678831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:06.803500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:06.926170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:07.142594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:07.259281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:07.396912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:07.516593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:07.643252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:07.762933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:07.889596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:08.024267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:08.145910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:08.259604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:08.380282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:08.494011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:08.612661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:08.742313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:08.851023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:08.967744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:09.073430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:09.183136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:09.301818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:09.418508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:09.529210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:09.634927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:09.753611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:09.877312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:10.009958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:10.230364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:10.345061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:10.447753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:10.550511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:10.660219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:10.769892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:10.894559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:11.007257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:11.106993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:11.207722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:11.311446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:11.427135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:11.532854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:11.650538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:11.759248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:11.872972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:11.977692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:12.089365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:12.197109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:12.307813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:12.427461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:12.536171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:12.644906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:12.777525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:12.886234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:12.994976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:13.209371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:13.324062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-03-05T21:27:19.108596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-05T21:27:19.314045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-05T21:27:19.518499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-05T21:27:19.724976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-03-05T21:27:19.952367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-03-05T21:27:13.540517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:13.898528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:14.214681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-05T21:27:14.557766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Sample

First rows

IdReveal StatusRarityRarity ScoreRankingGenderBreedFur ColorEyes ColorDominant PersonalityRecessive PersonalityVitalityIntelligenceRobustnessObedienceFriendlinessBonding
010NoneNaNNaNNoneNoneNoneNoneNoneNoneNaNNaNNaNNaNNaNNaN
120NoneNaNNaNNoneNoneNoneNoneNoneNoneNaNNaNNaNNaNNaNNaN
230NoneNaNNaNNoneNoneNoneNoneNoneNoneNaNNaNNaNNaNNaNNaN
341NoneNaNNaNNoneNoneNoneNoneNoneNoneNaNNaNNaNNaNNaNNaN
450NoneNaNNaNNoneNoneNoneNoneNoneNoneNaNNaNNaNNaNNaNNaN
560NoneNaNNaNNoneNoneNoneNoneNoneNoneNaNNaNNaNNaNNaNNaN
672Bronze36.06766.0FemaleHuskyBlack & white #11Brown #3NaiveDevoted5.05.05.05.03.01.0
780NoneNaNNaNNoneNoneNoneNoneNoneNoneNaNNaNNaNNaNNaNNaN
890NoneNaNNaNNoneNoneNoneNoneNoneNoneNaNNaNNaNNaNNaNNaN
9102Bronze50.03431.0FemaleHuskyBlack & white #9Green #4FunnySerious7.05.09.05.03.01.0

Last rows

IdReveal StatusRarityRarity ScoreRankingGenderBreedFur ColorEyes ColorDominant PersonalityRecessive PersonalityVitalityIntelligenceRobustnessObedienceFriendlinessBonding
799079912Diamond94.035.0FemaleGerman ShepherdSilver #9Brown #5SeriousIndependent5.010.03.012.03.01.0
799179922Silver47.04072.0FemaleShiba InuRed #9Brown #3TalkativeGentle6.08.011.03.03.01.0
799279932Bronze43.05111.0FemaleLabradorSand #8Brown #3ExcentricTalkative3.04.03.04.09.01.0
799379942Gold77.0611.0FemaleHuskyBlack & white #5Brown #5StubbornJolly7.08.09.03.03.01.0
799479952Bronze42.05366.0MaleHuskyBlack & white #11Blue & brown #3IndependentNeedy5.06.04.04.03.01.0
799579960NoneNaNNaNNoneNoneNoneNoneNoneNoneNaNNaNNaNNaNNaNNaN
799679972Bronze43.05111.0FemaleGolden RetrieverSand #8Black #4FashionistaJolly6.04.03.05.08.01.0
799779980NoneNaNNaNNoneNoneNoneNoneNoneNoneNaNNaNNaNNaNNaNNaN
799879992Bronze39.06122.0FemaleShiba InuBlack & tan #9Black #5TalkativeIndependent9.011.08.03.03.01.0
799980002Bronze40.05887.0FemaleFrench BulldogBlack & white #3Brown #4StubbornAdventurer8.03.03.08.013.01.0